Additive Manufacturing Process Monitoring Techniques

Additive manufacturing has matured into a vital technology for producing complex metal and polymer components in aerospace, medical and automotive industries. Ensuring part quality and repeatability requires real-time insight into dynamic phenomena such as melt-pool stability, thermal gradients, composition changes and defect formation. Process monitoring techniques range from optical and thermal imaging to spectroscopic analysis and acoustic sensing, often integrated with machine-learning algorithms to enable closed-loop control. These approaches provide non-contact, in-situ data on temperature fields, material emissions and refractive index variations, supporting adaptive parameter adjustment, early failure detection and the creation of digital twins. By correlating sensor outputs with microstructure, porosity and mechanical properties, modern monitoring strategies enhance reliability, reduce waste and accelerate certification pathways for critical applications.

Research from Nature Portfolio

Recent studies have demonstrated the power of plasma emission spectroscopy combined with machine learning to detect porosity during laser-based deposition of high-strength aluminium alloys. Time- and position-resolved spectral features were used to train a random forest classifier, achieving over eighty per cent precision in recognising pores in 7075-Al parts. Analysis of key emission lines revealed correlations between spectral kurtosis and thermal accumulation, offering a pathway to in-process quality assurance. In a parallel effort, emission spectra of the plasma plume during laser melting of AISI4140 steel were linked quantitatively to micro-hardness of the solidified zone. By adopting a dimensionless framework, spectral line intensities, laser energy density and scan speed were related through a piecewise function, yielding hardness predictions with mean errors below three per cent. These advances highlight the feasibility of spectroscopy-based monitoring for direct feedback on mechanical properties in situ.

Additive Manufacturing Process Monitoring Techniques publication trend

The graph below shows the total number of articles in additive manufacturing process monitoring techniques across all publications each year (not limited to Nature Index journals).

Technical terms

Additive manufacturing: A layer-by-layer fabrication process for creating three-dimensional parts directly from digital models.

Melt pool: The localized region of molten material created by a heat source during metal additive manufacturing.

Plasma emission spectroscopy: A technique that analyses the light emitted by excited atoms and ions in a plasma plume to infer temperature and chemical composition.

Thermal spectral imaging: Non-contact measurement of surface temperature and morphology by capturing emitted thermal radiation across multiple wavelengths.

Schlieren imaging: An optical method that visualises refractive index variations in transparent gases, revealing flow and thermal gradients.

Coaxial monitoring: Sensor configuration in which detection optics are aligned with the process beam to observe the melt pool or plume along the same axis.

Random Forest classifier: An ensemble machine-learning algorithm that constructs multiple decision trees to perform classification tasks on complex data.

Dimensionless analysis: A scaling approach that expresses physical relationships independent of units, facilitating comparisons across different processing conditions.

References

1. Review on Quality Control Methods in Metal Additive Manufacturing. Applied Sciences (2021).
2. In-situ porosity recognition for laser additive manufacturing of 7075-Al alloy using plasma emission spectroscopy. Scientific Reports (2020).
3. In-situ Monitoring on Micro-hardness of Laser Molten Zone on AISI4140 Steel by Spectral Analysis. Scientific Reports (2020).
4. Enhancing laser cladding stability: Defects and schlieren-based analytics during L-DED. Additive Manufacturing (2025).
5. A Review of Thermal Spectral Imaging Methods for Monitoring High-Temperature Molten Material Streams. Sensors (2023).
6. Angular dependence of coaxial and quasi-coaxial monitoring systems for process radiation analysis in laser materials processing. Optics and Lasers in Engineering (2022).